Optimisation of Radio Frequency Assisted Extraction of Apple Peel Extract: Total Phenolic Contents and Antioxidant Activity
Bibliographic record
Abstract
The objective of this study is to determine the optimum operating conditions of apple peel extraction process using a novel extraction method known as radio frequency assisted extraction (RFAE). Apple peel extract with the highest total phenolic contents and antioxidant activity is targeted in this study. RFAE is a green extraction method which operates based on the dielectric heating concept. Response surface methodology was employed for designing the experiment, analysing the effects and optimising the extraction conditions for RFAE of apple peel phenolic compounds. The effects of ethanol concentration (10 – 70 %), mixing speed (10– 320 mL N2/min), solid to liquid ratio (0.002 - 0.02 g/mL) and RF power (200 – 400 W) on total phenolic compounds and DPPH radical scavenging activity efficiency were investigated in order to understand and improve the RFAE performance. All processing parameters exert significant effects on the total phenolic compounds and radical scavenging activity of the apple peel extract. The highest recovery of TPC with a value of 121.87 mg GAE/ g DW can be achieved if the extraction is performed using 42.67 % ethanol concentration, mixing speed of 320 mL N2/min, solid to liquid ratio of 0.0114 g/mL and power at 400 W. The maximum value of DPPH (94.54 %) can be achieved if the extraction is performed using an ethanol concentration, mixing speed, solid to liquid ratio and power of 37.33 %, 165 mL N2/min, 0.02 g/mL and 400 W.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".